Method and system for targeted advertising based on topical memes

ABSTRACT

A targeted advertising system and method based on memes contained in content sources are disclosed. Content matching keywords-defining topics are identified from content sources and are further processed to extract the memes. Ad networks servicing the content are also identified and their reach for each meme determined. The system and method extract also viral dynamics of the content associated to a meme and use the aggregation of the viral dynamics as a measure of engagement level for the meme. The system and method allow a Marketer to select a meme based on the engagement level and to run an ad campaign against the meme. The advertisements are delivered through an Ad network and inserted at the meme page level when the content hosting the meme is accessed, the Ad network being selected based on its reach.

RELATED APPLICATIONS

The present application claims benefit from the U.S. provisionalapplication to Christopher NEWTON et al. Ser. No. 61/023,187 filed onJan. 24, 2008 entitled “Method and System for Targeted Advertising Basedon Topical Memes”, which is incorporated herein by reference.

FIELD OF INVENTION

The present patent application relates to a computer implemented methodand system for targeted advertising, and in particular, to a computerimplemented method and system for targeted advertising based on topicalmemes.

BACKGROUND OF THE INVENTION

Current on-line advertisement targeting methods are either site based,keyword based, contextual, or demographic based.

Site based targeted advertising involves a media buyer deciding to runadvertisements, to be also briefly referred to as “Ads”, on a specificproperty based on their knowledge of the property.

Keyword based targeted advertising involves a media buyer selectingkeywords, and Ad networks delivering Ads to web pages, which contain thecontent including those keywords. This method of serving Ads means thata media buyer's Ad may be seen on thousands of web sites that happen tocontain the keywords being used. These methods, although widely used insocial media web sites, do not accurately target Ads to the interest ofend users, which may not be related to selected keywords but rather tothe points of discussions typically called also memes. The rise ofsocial media means that there are millions of conversations going on atany time. Those conversations evolve into multiparty (multi-site) memes.Often these memes can be very beneficial to one or more brands, even ifthe memes are negative.

Marketers would be very much interested to launch ads targeted againstthese memes, but today no method or system exists to identify memesamongst the millions of conversations and to target Ads against thosememes.

Accordingly, there is a need in the industry for the development of anautomated method and system for targeted advertising against memes,which would be more specific to the interests of end users.

SUMMARY OF THE INVENTION

There is an object of the invention to provide a method and system fortargeted advertising based on topical memes, which would cater to theinterest of the end users.

According to one aspect of the present invention, a method for targetingadvertisement is disclosed, the method comprising steps of:

(a) selecting a meme;

(b) identifying web pages containing the selected meme;

(c) selecting an advertising network servicing a number of contentsources hosting said web pages;

(d) selecting an advertisement assigned to the selected meme;

(e) delivering said selected advertisement to said number of saidcontent sources through said advertising network; and

(g) inserting the selected advertisement into the web pages.

The step of selecting the meme comprises:

-   -   i) retrieving content matching a selected topic;    -   ii) extracting a set of memes from the matching content;    -   iii) associating each meme of the set of memes with its        associated content, wherein each associated content includes a        web page containing said each meme; and    -   iv) storing the set of memes along with their respective        associated content in a database.        Furthermore, the method comprises:

extracting viral dynamics of the content matching the selected topic;and

for said each meme, aggregating the viral dynamics of the contentassociated with said each meme.

Beneficially, the method further comprises storing aggregate values ofthe viral dynamics along with their associated meme in a database

In one modification, the step of extracting the set of memes comprisesapplying a feature extraction algorithm to said matching content.

In another modification step (c) of the method comprises:

-   -   i) extracting a list of advertising networks servicing the        content sources; and    -   ii) selecting, from said list, the advertising network having a        widest reach.

In a further modification step (d) of the method comprises:

-   -   i) setting an advertisement and a deployment threshold for said        advertisement;    -   ii) comparing the deployment threshold with an aggregate value        of viral dynamics associated with the selected meme; and    -   iii) assigning the advertisement to the selected meme provided        the deployment threshold matches the aggregate value of the        viral dynamics associated with the selected meme.

Advantageously, the method further comprises:

maintaining the assigned advertisement on said web pages provided thatthe aggregate value of the viral dynamics associated with the selectedmeme is above the deployment threshold; and

removing the selected advertisement from said web pages provided thatthe aggregate value of the viral dynamics associated with the selectedmeme is below the deployment threshold.

According to another aspect of the present invention a method oftargeting advertisement is disclosed, the method comprising:

(a) selecting a topic;

(b) retrieving content matching the selected topic;

(c) extracting a meme from the matching content; and

(d) running an advertisement campaign against said meme wherein saidadvertisement campaign is targeted to pages containing the meme

Advantageously, said meme is extracted by applying a feature extractionalgorithm to the matching content.

Furthermore, the method comprises extracting viral dynamics of thematching content and aggregating the viral dynamics of a subset contentassociated with selected meme to determine an aggregate value of viraldynamics associated with the selected meme wherein the subset content isa subset of the matching content.

Beneficially, the method comprises storing the aggregate value of theviral dynamics in time series.

In one modification, the method further comprises terminating theadvertisement campaign when said aggregate value of the viral dynamicsis below a threshold.

In another modification step (d) of the method comprises:

-   -   i) selecting an advertising network from a list of advertising        networks;    -   ii) selecting an advertisement to run against the selected meme;        and    -   iii) inserting the advertisement into selected pages of said        matching content containing the meme, wherein said advertisement        is delivered through the selected advertising network.

Furthermore the advertising network is selected to maximize a number ofpages containing the selected meme that can be reached by theadvertisement campaign.

In yet another aspect of the present invention a system for performing ameme-based targeted advertising is disclosed, the system comprising:

a computer, having a processor and a computer readable storage mediumstoring computer readable instructions for execution by the processor,to form the following modules:

-   -   (a) a first processing module operably connected to one or more        content sources for extracting a meme from content matching a        selected topic, and for associating the meme to a subset content        containing the meme wherein the subset content is a subset of        said content matching the selected topic;    -   (b) a second processing module operably connected to said first        processing module for selecting advertising networks servicing        said content sources; and    -   (c) a third processing module operably connected to said first        and second processing module for delivering advertisements        through the selected advertising networks to web pages        containing the meme and included in the subset content.

The system further comprises a viral dynamics extraction module forextracting viral dynamics of the matching content and aggregating theviral dynamics of the subset content.

Furthermore the system comprises a database stored in a computerreadable storage medium for storing aggregate values of the viraldynamics.

Advantageously, the system further comprises an analysis module foranalyzing the content against the selected topic defined by a set ofkeywords and for identifying the content matching the selected topic.

Beneficially, the system further comprises an advertisement matchingmodule for selecting one or more advertisements matching the meme andfor comparing a deployment threshold associated with said one or moreadvertisements with an aggregate value of viral dynamics associated tosaid meme.

In a further aspect of the present invention, it is disclosed a computerreadable medium, comprising a computer code instructions stored thereon,which, when executed by a computer, perform the steps of the methods ofthe present invention.

The present invention will be more fully understood from the followingdetailed description of the preferred embodiments that should be read inlight of the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of example,with reference to the accompanying drawings in which:

FIG. 1 is a block diagram illustrating a system for meme-based targetedadvertisement according to the embodiment of the present invention;

FIG. 2 illustrates an operation of the “Meme Clustering and ViralDynamics Extraction” module of FIG. 1;

FIG. 3 illustrates a structure of the “Meme Clustering and ViralDynamics Extraction” module of FIG. 1;

FIG. 4 illustrates steps of a method for extracting and sortingadvertising networks;

FIG. 5 illustrates a sub-system for extracting and sorting theadvertising networks, where the method of FIG. 4 is implemented;

FIG. 6 illustrates steps of a method for matching and delivery ofadvertisements; and

FIG. 7 illustrates a sub-system for Matching and Delivery ofadvertisements, where the method of FIG. 6 is implemented.

DETAILED DESCRIPTION OF THE EMBODIMENTS OF THE INVENTION

Embodiments of the invention describe a system for supporting theextraction of memes and associated viral dynamics from content sources,and methods for delivering Ads targeted to selected memes using such asystem. With reference to the drawings, in general, and FIGS. 1 to 7, inparticular, the method and system of the present invention aredisclosed.

FIG. 1 illustrates a system 100 for meme-based targeted advertisementaccording to an embodiment of the present invention. The system 100comprises a first processing module represented by a Meme Clustering andViral Dynamics Extraction Module 110 communicating to content sources160 via a public network 140. The public network 140 can be theInternet, a Public Switched Telephone Network (PSTN), a mobile network,or any other network providing connectivity to content sources 160.

Content sources 160 are publicly available sources of media ormultimedia content such as web content including text, audio, video,images or any combination thereof. The content sources 160 can includeon-line publications by social media communities, such as blogosphereshosting various content, for example, web posts, articles, websites,consumer generated audio and/or videos, consumer generated images or anyother content that the system of the present invention can accessthrough the public network 140. Each publication of the content may haveone or more pages for text-based content, and/or one or more parts foraudio, video or image-based content.

The Meme Clustering and Viral Dynamics Extraction module 110, alsoreferred to as meme module 110 comprises a hardware platform, forexample, a general purpose or specialized computer, including a centralprocessing unit (CPU), and a computer readable medium, (e.g., a memoryand other storage devices such as CD, DVD, hard disk drive, etc) havinginstructions stored thereon for execution by the CPU.

The Meme Clustering and Viral Dynamics Extraction module 110 is providedfor extracting and clustering memes, also called topical memes, withintopics discussed in the accessed content, and for further storing theclustered topical memes. The Meme Clustering and Viral DynamicsExtraction module 110 also performs the extraction of viral dynamicsassociated with the content retrieved from the content sources 160 andfurther stores the viral dynamics for each piece of content in adatabase. The meme module 110 will be described in more detailshereinafter with reference to FIGS. 2 and 3 below.

As shown in FIG. 1, the meme module 110 is connected to a secondprocessing module represented by an Ad Networks Extraction and Sortingmodule 130 as well as to a third processing module represented by an AdMatching and Delivery module 120.

The Ad Networks Extraction and Sorting module 130 comprises a hardwareplatform, for example, a general purpose or specialized computer,including a central processing unit (CPU), and a computer readablemedium, (e.g., a memory and other storage devices such as CD, DVD, harddisk drive, etc) having instructions stored thereon for execution by theCPU. The Ad Networks Extraction and Sorting module 130 identifies andsorts advertising networks, for brevity Ad networks, that runadvertisements (Ads) on content sources 160 associated with the storedtopical memes. Ad networks are typically advertisement delivery networkssuch as those managed by Google, Yahoo, Doubleclick, ValueClick Media orother known Ad networks, which deliver selected Ads to selected webcontent or pages according to set criteria. The extraction and sortingmodule 130 will be described in more detail with regard to FIGS. 4 and 5below.

The Ad Matching and Delivery module 120 comprises a hardware platform,for example, a general purpose or specialized computer, including acentral processing unit (CPU), and a computer readable medium, (e.g., amemory and other storage devices such as CD, DVD, hard disk drive, etc)having instructions stored thereon for execution by the CPU. The AdMatching and Delivery module 120 matches Ads to a selected meme anddelivers the Ads through the Ad Network 150 to content sources 160associated with the selected meme. In one embodiment, the Ad delivery iscarried out according to predetermined targeting criteria related, interalia, to the Ad Network 150 and the content sources 160. The Ad Matchingand Delivery module 120 will be described in more detail with regard toFIGS. 6 and 7 below.

Companies are generally interested in running Ads that are specificallyplaced on content related to specific topics, and optionally, revolvingaround a particular meme or point of discussion. Accordingly, the system100 of the embodiment of the invention identifies the content related toa certain topic and extracts respective topical memes. These functionsare performed by the “Meme Clustering and Viral Dynamics Extraction”module 110 shown in FIG. 1.

he system 100, including all modules illustrated in FIG. 1, can beimplemented in one or more software modules running on a hardwareplatform, comprising computer readable instructions stored in a computerreadable medium, for example, a general purpose or specialized computer,including a central processing unit (CPU), and a computer readablemedium having instructions stored thereon, e.g., a memory and otherstorage devices such as CD, DVD, hard disk drive, etc. As an example,the modules of the system 100 can be implemented as individual softwaremodules running on the same hardware platform. Alternatively, themodules of the system 100 can be implemented on different hardwareplatforms, e.g., on different computers connected in a network. Otherimplementations are possible and are well known to the persons skilledin the art.

The operation of the “Meme Clustering and Viral Dynamics Extraction”module 110 will now be described in more detail with reference to FIG.2.

FIG. 2 shows a flowchart 200 illustrating the operation of the mememodule 110 of FIG. 1, including steps of generating topical memes, andextracting viral dynamics of posts associated to the memes. Contentsources 160, which are also shown in FIG. 1, represent web sources orother on-line social media communities as described earlier that areaccessed by crawl sources at step 250 to retrieve relevant content atstep 240. At step 220, the content 240 is analyzed against a topicdefined by keywords entered at step 210. By way of example, the keywordsdefining the topics can be “US Politics” for a topic profile, and“Barack Obama” and/or “Hilary Clinton” as the keywords. Existing topicsand associated keywords are stored in a database (not shown), comprisingcomputer readable instructions stored in a computer readable storagemedium, such as computer memory, CD-ROM, DVD, floppy, tape or otherstorage medium, and new keywords defining new topics may be inputtedthrough a graphical user interface (not shown) to the system 100 of FIG.1.

The step 220 analyzes the keywords against the content 240 retrievedfrom the content sources 260, and step 230 identifies the content thatmatches the keywords defining selected topic.

At step 270 named “Cluster Memes within Topic”, the content identifiedat the step 230, is processed to extract points of discussion, ortopical memes, associated with the content. The extraction of the memescan be performed using independent feature extraction algorithms thatare known in the data analysis field. An example of a feature extractionalgorithm that can be used at step 270 is a Non-Negative MatrixFactorization, see, for example an article in Wikipedia entitled“Non-Negative Matrix Factorization” cited in the Information DisclosureStatement for this application. Other algorithms such as PrincipalComponent Analysis (PCA) or other algorithms described, e.g., in thebook entitled “Programming Collective Intelligence” by Toby Segaranpublished by O'Reilly Media press in August 2007, which is incorporatedherein by reference, could be used to extract topical memes from on-linecontent.

The topical memes thus extracted are further clustered. In theembodiment of the invention, each extracted topical meme is associatedwith a subset content, which contain the meme. This subset content is asubset of the content matching the selected topic. At step 280 of theflowchart 200, the clustered topical memes are stored along with theirassociated subset content in a database stored in a computer readablestorage medium (not shown). Following the example of keywords definingtopics provided above, the step 270 “Cluster Meme Within Topic” couldfind the following topical memes: “Barak Obama in the lead in primaries”with 7 more articles related; and “Hillary Clinton disagrees with Obamaon key points” with 12 more related articles. These 2 topical memeswould then be stored in the database at step 280 along with theirrespective subsets content.

In the embodiment of the present invention an advertising campaign isrun against one or more selected memes according to certain engagementmetrics thresholds related to the memes.

Engagement metrics, also to be referred to as viral dynamics, aredefined as various social media popularity metrics, such as total memecomment count, unique commenter count, inbound link count, breadth ofreply, views, bookmarks, votes, buries, favorites, awards, acceleration,momentum, subscription counts, replies, spoofs, ratings, friends,followers, and updates, etc. Other viral dynamics that can be extractedfrom the content are listed in the co-pending U.S. patent applicationSer. No. 12/174,345 filed Jul. 18, 2008 entitled “Method And System ForDetermining Topical On-Line Influence Of An Entity”, which isincorporated herein by reference.

In the embodiment of the present invention, the viral dynamics for eachpost or piece of content are extracted at step 290 of flowchart 200 and,at step 295, the viral dynamics per post are stored in a database (notshown) having computer readable instructions stored in a computerreadable storage medium.

Referring back to the above example, for the meme of “Barak Obama in thelead in primaries”, each of the 7 related articles are processed, andthe viral dynamics or each article are extracted as described above.

FIG. 3 shows a sub-system 300 for implementing the “Meme Clustering andViral Dynamics Extraction” module 110 of FIG. 1, which operation hasbeen described with regard to FIG. 2 above.

The sub-system 300 includes a Collection Engine module 330 connected tocontent sources 160 for retrieving content such as articles, posts andmultimedia data as described earlier in accordance with FIG. 1. TheCollection Engine Module 330 can take the form of a search engine,internet crawler or other collections mechanisms that can be used toaccess public data and retrieve its content.

An Analysis module 380 is connected to the Collection Engine 330, and toa Topic Database 310 storing keyword-defining topics. The Analysismodule 380 performs a Topic versus Content analysis to identify contentthat matches the topics. In another embodiment, the Analysis module 380has a graphical user interface to allow a user to enter keywords and/ortopics that can be used to identify matching content.

A Meme Clustering module 320 is connected to the Analysis module 380 forreceiving the matching content identified by the Analysis module 380.The Meme Clustering module 320 runs feature extraction and clusteringalgorithms to identify memes or distinct conversation points present inthe matching content, and to further cluster the memes along with theirassociated subset content. The algorithms applied by the clusteringmodule 320 are described above in step 270 of FIG. 2. The memesextracted within a topic are thus grouped along with their associatedsubset content and stored in a Topical Memes Database 370. In theembodiment, each cluster includes a meme and a subset content in whichthe meme appears.

The Topic vs Content Analysis Module 380 is also connected to a ViralDynamics Extraction module 340. The Viral Dynamics Extraction module 340measures the engagement level for each piece of content as describedwith respect to step 290 of FIG. 2 and as further described in detail inthe co-pending provisional application cited above. The viral dynamicsof the content obtained by the module 340 are stored in a viral dynamicsdatabase 350.

The sub-system 300 can be implemented as a single software modulerunning on a hardware platform, including computer readable instructionsstored in a computer readable medium, for example, a general purpose orspecialized computer, including a central processing unit (CPU), and acomputer readable medium, e.g., a memory and other storage devices suchas CD, DVD, hard disk drive, etc. containing instructions for executionby the CPU and performing the functions of the module 380, module 320,module 330 and module 340 described above.

Alternatively, the sub-system 300 can be implemented as a distributedplatform, including module 380, module 320, module 330 and module 340implemented individually, or in selective groupings, for example, asdedicated server computers interconnected by a bus, a local and/or awide area network using a wired, a wireless medium or a combinationthereof. Each module implemented as a server computer includes aprocessor and computer readable instructions stored in a computerreadable medium for execution by the processor and performing thefunctions of the module. The computer readable medium, includes, amemory and other storage devices such as CD, DVD, hard disk drive, etc.

Each of the database modules 350, 370 and 310 can be of any type ofcommercial or proprietary database that allows data to be accessed forread and write operations and includes a computer readable medium, e.g.,a memory and other storage devices such as CD, DVD, hard disk drive,etc., and instructions stored thereon for performing various functionsof the database

The method and system of the embodiments of the present inventionprovide the flexibility of selecting an Ad Network through which anadvertising campaign can be run according to certain performancecriteria, e.g., its level of reach for a selected meme. The level ofreach in this instance refers to the number of social web sites or pageswithin those websites containing the selected meme and serviced by theAd network.

FIG. 4 shows a flowchart 400 illustrating an Ad network extraction andsorting method as well as the aggregation of the viral dynamicsextracted from a set of content related to a meme. The topical memes 470and the Per Post Viral Dynamics 450 generated previously in theflowchart 200 of FIG. 2 at steps 270 and 290, respectively, are used asinputs to the “Aggregate per Post Dynamics Per Meme” step 410. At step410, the viral dynamics of all posts or pieces of content within asubset content are aggregated for each corresponding topical meme, andat step 420, the list of topical memes is sorted according to theiraggregate value of viral dynamics.

The aggregate value, in this instance, refers to the accumulation ofviral dynamics across all the posts or pieces of content within a subsetcontent associated with a topical meme. This accumulation can be doneover time during which the viral dynamics for any new post is added tothe aggregate value. Alternatively, the aggregation can be normalizedagainst a selected period of time wherein the total count of viraldynamics per post is normalized against the duration of the post. Othermethods for measuring the level of engagement over a period of time orfor determining the most active meme can as well be adopted, includingrecording the aggregate values of the viral dynamics of the posts intime series to monitor the evolution of the viral dynamics over a periodof time.

At step 430, the Ad networks supporting the delivery of advertisementsare identified and extracted. At step 440, the extracted list of Adnetworks is sorted and the sorted list is further stored in a database(not shown). The sorted list of Ad networks allows the system and themethod of the present invention to determine for each meme, the level ofreach of the Ad networks.

Referring back to the above example, the method of flowchart 400 canfind that Google is present on 5 of the 7 sites containing the 7articles related to the meme “Barak Obama in the lead in primaries”, andYahoo is present only on the 2 remaining sites. In this case running anadvertising campaign through the Google network would provide a widerreach than through the Yahoo network assuming that the meme viraldynamics are the same on all sites.

The method of flowchart 400 of FIG. 4 can be implemented using asub-system 500 of FIG. 5, in which processing modules “Per Meme PostDynamics Aggregation Module” 510 and “Ad Network Extraction/SortingModule” 520 are interconnected with databases (Topical meme database570, Viral Dynamics database 550 and Ad networks database 530) tosupport the Ad networks extraction and sorting features of theembodiment of the present invention.

The Per Meme Post Dynamics Aggregation module 510 receives a topicalmeme from the Topical Memes database 370, and also receives the viraldynamics associated with the received topical meme stored in the ViralDynamics database 350 to aggregate the count of viral dynamics per postas described above with regard to step 410 of FIG. 4. This aggregationallows the system to determine the most active memes. This informationis used by an Ad campaigner/marketer to select a meme against which hecan run an advertising campaign. The Ad Networks Extraction and Sortingmodule 520, in the present embodiment, retrieves the identity of Adnetworks providers from the content associated to the topical memesstored in the database 370 and sorts them according to their level ofreach. This extraction and sorting, as described previously, can be usedby an Ad campaigner to select the Ad Network that would provide a widerreach. The sorted list of Ad Networks is stored in a database 530. TheAd Networks Database 530 can be of any type of commercial or proprietarydatabase that allows data to be accessed for read and write operationsand includes a computer readable medium, e.g., a memory and otherstorage devices such as CD, DVD, hard disk drive, etc., and instructionsstored thereon for performing various functions of the database FIG. 6shows an Ad Matching and Delivery flowchart 600 illustrating a method ofperforming targeting and delivery of meme page-level advertisement. Memepage-level advertisement targeting refers to the targeting of anadvertisement to a page containing the selected meme. In thisembodiment, an Ad campaigner selects a topical meme at step 660 from aset of Topical memes 670, followed by an automatic retrieval of relatedarticles or posts at step 650. In addition, the Ad campaigner creates alist of Ads at step 610, and at step 620, performs an Ad-to-Mememapping, which results in the selection of Ads to be run against thetopical meme selected at step 660.

In one embodiment, each Ad is associated with a deployment threshold,which determines when the Ad can be used in an Ad campaign, or withdrawnfrom the Ad campaign. In this embodiment, an Ad is maintained in an Adcampaign as long as the aggregate value of the viral dynamics associatedwith the meme is within a specified boundary or above the deploymentthreshold for the Ad. The Ad would be withdrawn from advertising, whenthe viral dynamics falls out of the boundary or below the deploymentthreshold.

At step 640, the selected Ads of step 620 are assigned to specific AdNetworks provided from the list of Ads Networks 630 extracted from theflowchart 400 of FIG. 4. At step 680, a meme page-level targeting isperformed to target Ads to web pages containing the selected meme. TheAds are thus delivered through the Ad Network to an end user viewing thetargeted web pages.

The Ad Matching and Delivery method of FIG. 6 can be implemented using asub-system 700 of FIG. 7. In the sub-system 700, an Ad-to-Meme Matchingmodule 760 is provided to match Ads stored in the Ads database 710 tothe selected meme from the topical meme database 370. As an example, ameme slamming a specific feature of the new Apple iPhone is beneficialto Nokia, and they might want to run an advertisement in the specificcontent that is part of the meme. In this case, an Ad of Nokia toutingthe benefits of the Nokia alternative to that specific feature of iPhonewould then be mapped to the meme.

The Topical Meme database 370 is connected to a Meme site/pageidentification module 720, which identifies the page where the meme ispresent from the content associated with the meme. This identificationof the meme page level allows the delivery of Ads at the meme page levelusing the Ad Targeting and Delivery module 730. The Ad Targeting andDelivery module 730 is provided to interface the Ad Network 750. Thismodule 730 sends the selected Ads through the Ad network 750 to thewebsite 740 hosting content for insertion into the meme page when thecontent is accessed.

The embodiments of the present invention provides numerous advantages byallowing marketers to target end users according to their interestreflected by the memes buried in the content they access and that canonly be unearthed with a fine-tune analysis as set forth in the presentinvention.

Often these memes can be very beneficial to one or more brands, even ifthe meme is negative. For instance as stated earlier, a meme slammingthe new Apple iPhone is beneficial to Nokia, and Nokia might want to runan advertisement in the specific content that is part of the meme.Nokia's Ad may specifically talk to the problems noted in the iPhonememe, and also talk about the benefits of the alternative provided byNokia. Such an Ad would be useless shown on another content, which isnot related to this meme.

The embodiments of the present invention can keep track of the number ofmemes launching every day, and millions of sources that they can eruptfrom. By using the methods of the embodiments of the present invention,someone can research all the sites and content involved in a selectedmeme, find all the pages containing the meme, and run a dynamic Adcampaign against the pages according to a comparison between the viraldynamics of the content and the deployment threshold of the Ad.

Although the invention has been illustrated with the reference tospecific embodiments, it will be apparent to those skilled in the artthat various changes and modifications may be made which clearly fallwithin the scope of the invention. The invention is intended to beprotected broadly within the spirit and scope of the appended claims.

What is claimed is:
 1. A computer implemented method for targetingadvertisement, comprising steps of: analyzing, by a computer-basedsystem, web content against a defined topic; obtaining, by thecomputer-based system and based on the analyzing, topic-matching contentrelated to the defined topic; processing, by the computer-based system,the topic-matching content; extracting, by the computer-based system andbased on the processing, topical memes associated with thetopic-matching content, wherein each of the extracted topical memes is apoint of discussion within the defined topic; measuring, by thecomputer-based system, an engagement level for each piece of thetopic-matching content; obtaining, by the computer-based system andbased on the measuring, a respective viral dynamics metric for each saidpiece of the topic-matching content, wherein a viral dynamics metricindicates popularity of the piece of the topic-matching content;accumulating, by the computer-based system and for each of the extractedtopical memes, the viral dynamics metrics of the pieces of thetopic-matching content; obtaining, by the computer-based system andbased on the accumulating, a respective aggregate viral dynamics valuefor each of the extracted topical memes; determining, by thecomputer-based system, a most active meme from the extracted topicalmemes, based on the aggregate viral dynamics value for each of theextracted topical memes; identifying, by the computer-based system, webpages containing the most active meme; selecting, by the computer-basedsystem, an advertising network servicing a number of content sourceshosting the identified web pages; selecting, by the computer-basedsystem, an advertisement assigned to the most active meme; anddelivering, by the computer-based system, said selected advertisement tosaid number of said content sources through said advertising network. 2.The method of claim 1, further comprising storing the aggregate viraldynamics value for each of the extracted topical memes along with theirassociated extracted topical meme in a database.
 3. The method of claim1, wherein the step of processing the topic-matching content comprisesapplying a feature extraction algorithm to said topic-matching content.4. The method as described in claim 1, wherein the step of selecting theadvertising network comprises: extracting a list of advertising networksservicing the content sources; and selecting from said list theadvertising network, having a widest reach.
 5. The method of claim 1,wherein the step of selecting the advertisement assigned to the mostactive meme comprises: setting an advertisement deployment threshold forsaid advertisement; comparing the deployment threshold with theaggregate viral dynamics value associated with the most active meme; andassigning the advertisement to the most active meme provided thedeployment threshold matches the aggregate viral dynamics valueassociated with the most active meme.
 6. The method of claim 5, furthercomprising: maintaining the assigned advertisement on said web pagesprovided that the aggregate viral dynamics value associated with themost active meme is above the deployment threshold; and removing theassigned advertisement from said web pages provided that the aggregateviral dynamics value associated with the most active meme is below thedeployment threshold.
 7. A system for performing meme-based targetedadvertising, comprising: a computer, having a processor and a computerreadable storage medium storing computer readable instructions forexecution by the processor, wherein the instructions, when executed bythe processor, cause the computer to perform a method comprising:analyzing web content against a defined topic; obtaining topic-matchingcontent provided by one or more content sources, the topic-matchingcontent being related to the defined topic; processing thetopic-matching content; extracting topical memes associated with thetopic-matching content, wherein each of the extracted topical memes is apoint of discussion within the defined topic; measuring an engagementlevel for each piece of the topic-matching content; obtaining, based onthe measuring, a respective viral dynamics metric for each said piece ofthe topic-matching content, wherein a viral dynamics metric indicatespopularity of the piece of the topic-matching content; accumulating, foreach of the extracted topical memes, the viral dynamics metrics of thepieces of the topic-matching content; obtaining, based on theaccumulating, a respective aggregate viral dynamics value for each ofthe extracted topical memes; determining a most active meme from theextracted topical memes, based on the aggregate viral dynamics value foreach of the extracted topical memes; associating the most active meme tosubset content containing the most active meme, wherein the subsetcontent is a subset of said topic-matching content; selectingadvertising networks servicing said content sources; and deliveringadvertisements through the selected advertising networks to web pagescontaining the most active meme and included in the subset content. 8.The system as described in claim 7, further comprising a database storedin a computer readable storage medium for storing the aggregate viraldynamics values.
 9. The system as described in claim 7, wherein thedefined topic is defined by a set of keywords.
 10. The system asdescribed in claim 7, wherein the instructions, when executed by theprocessor, cause the computer to select one or more advertisementsmatching the most active meme, and cause the computer to compare adeployment threshold associated with said one or more advertisementswith the aggregate viral dynamics value associated to said most activememe.
 11. A non-transitory computer readable medium, comprising computercode instructions stored thereon, which, when executed by a computer,perform a method comprising: analyzing web content against a definedtopic; obtaining, based on the analyzing, topic-matching content relatedto the defined topic; processing the topic-matching content; extracting,based on the processing, topical memes associated with thetopic-matching content, wherein each of the extracted topical memes is apoint of discussion within the defined topic; measuring an engagementlevel for each piece of the topic-matching content obtaining, based onthe measuring, a respective viral dynamics metric for each said piece ofthe topic-matching content, wherein a viral dynamics metric indicatespopularity of the piece of the topic-matching content; accumulating, foreach of the extracted topical memes, the viral dynamics metrics of thepieces of the topic-matching content; obtaining, based on theaccumulating, a respective aggregate viral dynamics value for each ofthe extracted topical memes; determining a most active meme from theextracted topical memes, based on the aggregate viral dynamics value foreach of the extracted topical memes; identifying web pages containingthe most active meme; selecting an advertising network servicing anumber of content sources hosting the identified web pages; selecting anadvertisement assigned to the most active meme; and delivering saidselected advertisement to said number of said content sources throughsaid advertising network.